{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeEval parameter optimization result analysis (Part 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available result directories:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/results/2021-09-27_shared-optim'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-06_optim-part1'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_default-params-1&2&3&4-merged'),\n",
       " PosixPath('/home/projects/akita/results/.ipynb_checkpoints'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-07_optim-part2')]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Data path: /home/projects/akita/data/test-cases\n",
      "Result path: /home/projects/akita/results/2021-10-07_optim-part2\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "data_path = Path(\"/home/projects/akita/data\") / \"test-cases\"\n",
    "result_root_path = Path(\"/home/projects/akita/results\")\n",
    "experiment_result_folder = \"2021-10-07_optim-part2\"\n",
    "\n",
    "# build paths\n",
    "result_paths = [d for d in result_root_path.iterdir() if d.is_dir()]\n",
    "print(\"Available result directories:\")\n",
    "display(result_paths)\n",
    "\n",
    "result_path = result_root_path / experiment_result_folder\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Data path: {data_path.resolve()}\")\n",
    "print(f\"Result path: {result_path.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results and dataset metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from /home/projects/akita/results/2021-10-07_optim-part2\n"
     ]
    }
   ],
   "source": [
    "# load results\n",
    "print(f\"Reading results from {result_path.resolve()}\")\n",
    "df = pd.read_csv(result_path / \"results.csv\")\n",
    "\n",
    "# add dataset_name column\n",
    "df[\"dataset_name\"] = df[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "# load dataset metadata\n",
    "dmgr = Datasets(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract target optimized parameter names that were iterated in this run (per algorithm):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor (RR) ['bootstrap']\n",
      "XGBoosting (RR) ['booster']\n",
      "GrammarViz ['alphabet_size', 'paa_transform_size']\n",
      "ImageEmbeddingCAE ['latent_size']\n",
      "LSTM-AD ['lstm_layers']\n"
     ]
    }
   ],
   "source": [
    "algo_param_mapping = {}\n",
    "algorithms = df[\"algorithm\"].unique()\n",
    "param_ignore_list = [\"max_anomaly_window_size\", \"anomaly_window_size\", \"neighbourhood_size\", \"window_size\", \"n_init_train\", \"embed_dim_range\"]\n",
    "\n",
    "for algo in algorithms:\n",
    "    param_sets = df.loc[df[\"algorithm\"] == algo, \"hyper_params\"].unique()\n",
    "    param_sets = [json.loads(ps) for ps in param_sets]\n",
    "    param_names = np.unique([name for ps in param_sets for name in ps if name not in param_ignore_list])\n",
    "    search_space = set()\n",
    "    for param_name in param_names:\n",
    "        values = []\n",
    "        for ps in param_sets:\n",
    "            try:\n",
    "                values.append(ps[param_name])\n",
    "            except:\n",
    "                pass\n",
    "        values = np.unique(values)\n",
    "        if values.shape[0] > 1:\n",
    "            search_space.add(param_name)\n",
    "    algo_param_mapping[algo] = list(search_space)\n",
    "\n",
    "for algo in algo_param_mapping:\n",
    "    print(algo, algo_param_mapping[algo])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract optimized parameters and their values (columns: optim_param_name and optim_param_value) for each experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hyper_params(algo):\n",
    "    param_names = algo_param_mapping[algo]\n",
    "    def extract(value):\n",
    "        params = json.loads(value)\n",
    "        result = None\n",
    "        for name in param_names:\n",
    "            try:\n",
    "                value = params[name]\n",
    "                result = pd.Series([name, value], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "                break\n",
    "            except KeyError:\n",
    "                pass\n",
    "        if result is None:\n",
    "            return pd.Series([np.nan, np.nan], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "        return result\n",
    "    return extract\n",
    "\n",
    "df[[\"optim_param_name\", \"optim_param_value\"]] = \"\"\n",
    "for algo in algo_param_mapping:\n",
    "    df_algo = df.loc[df[\"algorithm\"] == algo]\n",
    "    df.loc[df_algo.index, [\"optim_param_name\", \"optim_param_value\"]] = df_algo[\"hyper_params\"].apply(extract_hyper_params(algo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract window size parameters (dependent params) and convert them into multiples of the dataset period size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependent_param_names = [\"neighbourhood_size\", \"window_size\"]\n",
    "\n",
    "def extract_window_param(value, param_name=\"\"):\n",
    "    params = json.loads(value)\n",
    "    try:\n",
    "        return params[param_name]\n",
    "    except KeyError:\n",
    "        return 0\n",
    "\n",
    "for param_name in dependent_param_names:\n",
    "    s_windows = df[\"hyper_params\"].apply(extract_window_param, param_name=param_name)\n",
    "    df2 = df[s_windows > 0][[\"dataset\"]].copy()\n",
    "    df2[param_name] = s_windows[df2.index]\n",
    "    df2[\"period_size\"] = df2[\"dataset\"].apply(lambda d: dmgr.get((\"GutenTAG\", d)).period_size)\n",
    "    df2[\"optim_param_name\"] = param_name\n",
    "    df2[\"optim_param_value\"] = df2[param_name] / df2[\"period_size\"]\n",
    "    df2[\"optim_param_value\"] = (df2[\"optim_param_value\"]\n",
    "                                .fillna(df2[param_name])\n",
    "                                .round(1)\n",
    "                                .replace(50., 0.5)\n",
    "                                .replace(100, 1.0)\n",
    "                                .replace(150, 1.5)\n",
    "                                .replace(200, 2.0))\n",
    "    df.loc[df2.index, [\"optim_param_name\", \"optim_param_value\"]] = df2[[\"optim_param_name\", \"optim_param_value\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, optim_params, repetition=1):\n",
    "    params_id = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1]) & (df[\"optim_param_name\"] == optim_params[0]) & (df[\"optim_param_value\"] == optim_params[1]), \"hyper_params_id\"].item()\n",
    "    path = (\n",
    "        result_path /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        dataset_id[1] /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define plotting functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = True\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "def plot_scores(algorithm_name, dataset_name, use_plotly: bool = default_use_plotly, **kwargs):\n",
    "    if isinstance(algorithm_name, tuple):\n",
    "        algorithms = [algorithm_name]\n",
    "    elif not isinstance(algorithm_name, list):\n",
    "        raise ValueError(\"Please supply a tuple (algorithm_name, optim_param_name, optim_param_value) or a list thereof as first argument!\")\n",
    "    else:\n",
    "        algorithms = algorithm_name\n",
    "    # construct dataset ID\n",
    "    dataset_id = (\"GutenTAG\", f\"{dataset_name}.unsupervised\")\n",
    "\n",
    "    # load dataset details\n",
    "    df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "\n",
    "    # check if dataset is multivariate\n",
    "    dataset_dim = df.loc[df[\"dataset_name\"] == dataset_name, \"dataset_input_dimensionality\"].unique().item()\n",
    "    dataset_dim = dataset_dim.lower()\n",
    "    \n",
    "    auroc = {}\n",
    "    df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "    skip_algos = []\n",
    "    algos = []\n",
    "    for algo, optim_param_name, optim_param_value in algorithms:\n",
    "        optim_params = f\"{optim_param_name}={optim_param_value}\"\n",
    "        algos.append((algo, optim_params))\n",
    "        # get algorithm metric results\n",
    "        try:\n",
    "            auroc[(algo, optim_params)] = df.loc[\n",
    "                (df[\"algorithm\"] == algo) & (df[\"dataset_name\"] == dataset_name) & (df[\"optim_param_name\"] == optim_param_name) & (df[\"optim_param_value\"] == optim_param_value),\n",
    "                \"ROC_AUC\"\n",
    "            ].item()\n",
    "        except ValueError:\n",
    "            warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n",
    "            auroc[(algo, optim_params)] = -1\n",
    "            skip_algos.append((algo, optim_params))\n",
    "            continue\n",
    "\n",
    "        # load scores\n",
    "        training_type = df.loc[df[\"algorithm\"] == algo, \"algo_training_type\"].values[0].lower().replace(\"_\", \"-\")\n",
    "        try:\n",
    "            df_scores[(algo, optim_params)] = load_scores_df(algo, (\"GutenTAG\", f\"{dataset_name}.{training_type}\"), (optim_param_name, optim_param_value)).iloc[:, 0]\n",
    "        except (ValueError, FileNotFoundError):\n",
    "            warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_name} with params {optim_params}.\")\n",
    "            df_scores[(algo, optim_params)] = np.nan\n",
    "            skip_algos.append((algo, optim_params))\n",
    "    algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "    if use_plotly:\n",
    "        return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "    else:\n",
    "        return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "\n",
    "def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "    # Create plot\n",
    "    fig = make_subplots(2, 1)\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=f\"channel-{i}\"), 1, 1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name=\"timeseries\"), 1, 1)\n",
    "    fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset[\"is_anomaly\"], name=\"label\"), 2, 1)\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[item], name=f\"{algo}={auroc[item]:.4f} ({optim_params})\"), 2, 1)\n",
    "    fig.update_xaxes(matches=\"x\")\n",
    "    fig.update_layout(\n",
    "        title=f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\",\n",
    "        height=400\n",
    "    )\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Create plot\n",
    "    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=f\"channel-{i}\")\n",
    "    else:\n",
    "        axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f\"timeseries\")\n",
    "    axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        axs[1].plot(df_scores.index, df_scores[item], label=f\"{algo}={auroc[item]:.4f} ({optim_params})\")\n",
    "    axs[0].legend()\n",
    "    axs[1].legend()\n",
    "    fig.suptitle(f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\")\n",
    "    fig.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PR_AUC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ROC_AUC</th>\n",
       "      <th>train_main_time</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
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       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>optim_param_name</th>\n",
       "      <th>optim_param_value</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">XGBoosting (RR)</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">booster</th>\n",
       "      <th>gblinear</th>\n",
       "      <td>0.006724</td>\n",
       "      <td>0.497804</td>\n",
       "      <td>0.480644</td>\n",
       "      <td>0.347727</td>\n",
       "      <td>0.845629</td>\n",
       "      <td>0.880401</td>\n",
       "      <td>30.069229</td>\n",
       "      <td>6.179861</td>\n",
       "      <td>168</td>\n",
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       "      <th>gbtree</th>\n",
       "      <td>0.007222</td>\n",
       "      <td>0.490050</td>\n",
       "      <td>0.488069</td>\n",
       "      <td>0.382022</td>\n",
       "      <td>0.842657</td>\n",
       "      <td>0.883112</td>\n",
       "      <td>4130.807396</td>\n",
       "      <td>7.550251</td>\n",
       "      <td>168</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Random Forest Regressor (RR)</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">bootstrap</th>\n",
       "      <th>True</th>\n",
       "      <td>0.005428</td>\n",
       "      <td>0.428245</td>\n",
       "      <td>0.369932</td>\n",
       "      <td>0.080776</td>\n",
       "      <td>0.803712</td>\n",
       "      <td>0.842361</td>\n",
       "      <td>686.905354</td>\n",
       "      <td>7.337085</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0.005481</td>\n",
       "      <td>0.370957</td>\n",
       "      <td>0.332235</td>\n",
       "      <td>0.087517</td>\n",
       "      <td>0.787820</td>\n",
       "      <td>0.819688</td>\n",
       "      <td>1135.938024</td>\n",
       "      <td>6.875242</td>\n",
       "      <td>168</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">LSTM-AD</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">window_size</th>\n",
       "      <th>2.0</th>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.327297</td>\n",
       "      <td>0.264651</td>\n",
       "      <td>0.132411</td>\n",
       "      <td>0.748477</td>\n",
       "      <td>0.831340</td>\n",
       "      <td>7212.296172</td>\n",
       "      <td>517.001906</td>\n",
       "      <td>193</td>\n",
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       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.192560</td>\n",
       "      <td>0.043615</td>\n",
       "      <td>0.003023</td>\n",
       "      <td>0.652914</td>\n",
       "      <td>0.679058</td>\n",
       "      <td>7187.589350</td>\n",
       "      <td>987.811766</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.191374</td>\n",
       "      <td>0.045169</td>\n",
       "      <td>0.013183</td>\n",
       "      <td>0.640939</td>\n",
       "      <td>0.651270</td>\n",
       "      <td>7100.157630</td>\n",
       "      <td>894.732521</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.166267</td>\n",
       "      <td>0.036323</td>\n",
       "      <td>0.046156</td>\n",
       "      <td>0.616039</td>\n",
       "      <td>0.613581</td>\n",
       "      <td>6811.646270</td>\n",
       "      <td>487.481813</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">lstm_layers</th>\n",
       "      <th>True</th>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.214740</td>\n",
       "      <td>0.076383</td>\n",
       "      <td>0.006863</td>\n",
       "      <td>0.661807</td>\n",
       "      <td>0.675470</td>\n",
       "      <td>6886.480288</td>\n",
       "      <td>231.708138</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.176170</td>\n",
       "      <td>0.038355</td>\n",
       "      <td>0.049062</td>\n",
       "      <td>0.628069</td>\n",
       "      <td>0.633170</td>\n",
       "      <td>7212.047548</td>\n",
       "      <td>657.695031</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.106736</td>\n",
       "      <td>0.024211</td>\n",
       "      <td>0.050602</td>\n",
       "      <td>0.570240</td>\n",
       "      <td>0.581969</td>\n",
       "      <td>7211.625977</td>\n",
       "      <td>1827.328234</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">ImageEmbeddingCAE</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">latent_size</th>\n",
       "      <th>70</th>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.355987</td>\n",
       "      <td>0.315853</td>\n",
       "      <td>0.106465</td>\n",
       "      <td>0.841322</td>\n",
       "      <td>0.939670</td>\n",
       "      <td>759.405784</td>\n",
       "      <td>25.712897</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0.000422</td>\n",
       "      <td>0.340344</td>\n",
       "      <td>0.243138</td>\n",
       "      <td>0.106465</td>\n",
       "      <td>0.814818</td>\n",
       "      <td>0.901283</td>\n",
       "      <td>720.849037</td>\n",
       "      <td>25.040023</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.284123</td>\n",
       "      <td>0.076797</td>\n",
       "      <td>0.143968</td>\n",
       "      <td>0.768262</td>\n",
       "      <td>0.856562</td>\n",
       "      <td>606.300005</td>\n",
       "      <td>25.297295</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">GrammarViz</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">paa_transform_size</th>\n",
       "      <th>5</th>\n",
       "      <td>0.006721</td>\n",
       "      <td>0.576626</td>\n",
       "      <td>0.684857</td>\n",
       "      <td>0.256584</td>\n",
       "      <td>0.913240</td>\n",
       "      <td>0.989016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.461379</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000664</td>\n",
       "      <td>0.511050</td>\n",
       "      <td>0.524767</td>\n",
       "      <td>0.171274</td>\n",
       "      <td>0.881226</td>\n",
       "      <td>0.980737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.362655</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.487485</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>0.004004</td>\n",
       "      <td>0.834552</td>\n",
       "      <td>0.966950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.302092</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">alphabet_size</th>\n",
       "      <th>6</th>\n",
       "      <td>0.000672</td>\n",
       "      <td>0.557095</td>\n",
       "      <td>0.589751</td>\n",
       "      <td>0.231264</td>\n",
       "      <td>0.903342</td>\n",
       "      <td>0.988678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.203208</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.487485</td>\n",
       "      <td>0.463138</td>\n",
       "      <td>0.004004</td>\n",
       "      <td>0.834552</td>\n",
       "      <td>0.966950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.905535</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.416830</td>\n",
       "      <td>0.266143</td>\n",
       "      <td>0.004004</td>\n",
       "      <td>0.742887</td>\n",
       "      <td>0.849363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.728401</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000672</td>\n",
       "      <td>0.380637</td>\n",
       "      <td>0.379138</td>\n",
       "      <td>0.002469</td>\n",
       "      <td>0.724865</td>\n",
       "      <td>0.754630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.743828</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     PR_AUC  \\\n",
       "                                                                        min   \n",
       "algorithm                    optim_param_name   optim_param_value             \n",
       "XGBoosting (RR)              booster            gblinear           0.006724   \n",
       "                                                gbtree             0.007222   \n",
       "                                                dart                    NaN   \n",
       "Random Forest Regressor (RR) bootstrap          True               0.005428   \n",
       "                                                False              0.005481   \n",
       "LSTM-AD                      window_size        2.0                0.000625   \n",
       "                                                1.5                0.000074   \n",
       "                                                True               0.000076   \n",
       "                                                0.5                0.000087   \n",
       "                             lstm_layers        True               0.000095   \n",
       "                                                3                  0.000081   \n",
       "                                                10.0               0.000076   \n",
       "ImageEmbeddingCAE            latent_size        70                 0.000115   \n",
       "                                                130                0.000422   \n",
       "                                                100                0.000355   \n",
       "GrammarViz                   paa_transform_size 5                  0.006721   \n",
       "                                                3                  0.000664   \n",
       "                                                4                  0.000502   \n",
       "                             alphabet_size      6                  0.000672   \n",
       "                                                4                  0.000502   \n",
       "                                                5                  0.000502   \n",
       "                                                3                  0.000672   \n",
       "\n",
       "                                                                             \\\n",
       "                                                                       mean   \n",
       "algorithm                    optim_param_name   optim_param_value             \n",
       "XGBoosting (RR)              booster            gblinear           0.497804   \n",
       "                                                gbtree             0.490050   \n",
       "                                                dart                    NaN   \n",
       "Random Forest Regressor (RR) bootstrap          True               0.428245   \n",
       "                                                False              0.370957   \n",
       "LSTM-AD                      window_size        2.0                0.327297   \n",
       "                                                1.5                0.192560   \n",
       "                                                True               0.191374   \n",
       "                                                0.5                0.166267   \n",
       "                             lstm_layers        True               0.214740   \n",
       "                                                3                  0.176170   \n",
       "                                                10.0               0.106736   \n",
       "ImageEmbeddingCAE            latent_size        70                 0.355987   \n",
       "                                                130                0.340344   \n",
       "                                                100                0.284123   \n",
       "GrammarViz                   paa_transform_size 5                  0.576626   \n",
       "                                                3                  0.511050   \n",
       "                                                4                  0.487485   \n",
       "                             alphabet_size      6                  0.557095   \n",
       "                                                4                  0.487485   \n",
       "                                                5                  0.416830   \n",
       "                                                3                  0.380637   \n",
       "\n",
       "                                                                             \\\n",
       "                                                                     median   \n",
       "algorithm                    optim_param_name   optim_param_value             \n",
       "XGBoosting (RR)              booster            gblinear           0.480644   \n",
       "                                                gbtree             0.488069   \n",
       "                                                dart                    NaN   \n",
       "Random Forest Regressor (RR) bootstrap          True               0.369932   \n",
       "                                                False              0.332235   \n",
       "LSTM-AD                      window_size        2.0                0.264651   \n",
       "                                                1.5                0.043615   \n",
       "                                                True               0.045169   \n",
       "                                                0.5                0.036323   \n",
       "                             lstm_layers        True               0.076383   \n",
       "                                                3                  0.038355   \n",
       "                                                10.0               0.024211   \n",
       "ImageEmbeddingCAE            latent_size        70                 0.315853   \n",
       "                                                130                0.243138   \n",
       "                                                100                0.076797   \n",
       "GrammarViz                   paa_transform_size 5                  0.684857   \n",
       "                                                3                  0.524767   \n",
       "                                                4                  0.463138   \n",
       "                             alphabet_size      6                  0.589751   \n",
       "                                                4                  0.463138   \n",
       "                                                5                  0.266143   \n",
       "                                                3                  0.379138   \n",
       "\n",
       "                                                                    ROC_AUC  \\\n",
       "                                                                        min   \n",
       "algorithm                    optim_param_name   optim_param_value             \n",
       "XGBoosting (RR)              booster            gblinear           0.347727   \n",
       "                                                gbtree             0.382022   \n",
       "                                                dart                    NaN   \n",
       "Random Forest Regressor (RR) bootstrap          True               0.080776   \n",
       "                                                False              0.087517   \n",
       "LSTM-AD                      window_size        2.0                0.132411   \n",
       "                                                1.5                0.003023   \n",
       "                                                True               0.013183   \n",
       "                                                0.5                0.046156   \n",
       "                             lstm_layers        True               0.006863   \n",
       "                                                3                  0.049062   \n",
       "                                                10.0               0.050602   \n",
       "ImageEmbeddingCAE            latent_size        70                 0.106465   \n",
       "                                                130                0.106465   \n",
       "                                                100                0.143968   \n",
       "GrammarViz                   paa_transform_size 5                  0.256584   \n",
       "                                                3                  0.171274   \n",
       "                                                4                  0.004004   \n",
       "                             alphabet_size      6                  0.231264   \n",
       "                                                4                  0.004004   \n",
       "                                                5                  0.004004   \n",
       "                                                3                  0.002469   \n",
       "\n",
       "                                                                             \\\n",
       "                                                                       mean   \n",
       "algorithm                    optim_param_name   optim_param_value             \n",
       "XGBoosting (RR)              booster            gblinear           0.845629   \n",
       "                                                gbtree             0.842657   \n",
       "                                                dart                    NaN   \n",
       "Random Forest Regressor (RR) bootstrap          True               0.803712   \n",
       "                                                False              0.787820   \n",
       "LSTM-AD                      window_size        2.0                0.748477   \n",
       "                                                1.5                0.652914   \n",
       "                                                True               0.640939   \n",
       "                                                0.5                0.616039   \n",
       "                             lstm_layers        True               0.661807   \n",
       "                                                3                  0.628069   \n",
       "                                                10.0               0.570240   \n",
       "ImageEmbeddingCAE            latent_size        70                 0.841322   \n",
       "                                                130                0.814818   \n",
       "                                                100                0.768262   \n",
       "GrammarViz                   paa_transform_size 5                  0.913240   \n",
       "                                                3                  0.881226   \n",
       "                                                4                  0.834552   \n",
       "                             alphabet_size      6                  0.903342   \n",
       "                                                4                  0.834552   \n",
       "                                                5                  0.742887   \n",
       "                                                3                  0.724865   \n",
       "\n",
       "                                                                             \\\n",
       "                                                                     median   \n",
       "algorithm                    optim_param_name   optim_param_value             \n",
       "XGBoosting (RR)              booster            gblinear           0.880401   \n",
       "                                                gbtree             0.883112   \n",
       "                                                dart                    NaN   \n",
       "Random Forest Regressor (RR) bootstrap          True               0.842361   \n",
       "                                                False              0.819688   \n",
       "LSTM-AD                      window_size        2.0                0.831340   \n",
       "                                                1.5                0.679058   \n",
       "                                                True               0.651270   \n",
       "                                                0.5                0.613581   \n",
       "                             lstm_layers        True               0.675470   \n",
       "                                                3                  0.633170   \n",
       "                                                10.0               0.581969   \n",
       "ImageEmbeddingCAE            latent_size        70                 0.939670   \n",
       "                                                130                0.901283   \n",
       "                                                100                0.856562   \n",
       "GrammarViz                   paa_transform_size 5                  0.989016   \n",
       "                                                3                  0.980737   \n",
       "                                                4                  0.966950   \n",
       "                             alphabet_size      6                  0.988678   \n",
       "                                                4                  0.966950   \n",
       "                                                5                  0.849363   \n",
       "                                                3                  0.754630   \n",
       "\n",
       "                                                                  train_main_time  \\\n",
       "                                                                             mean   \n",
       "algorithm                    optim_param_name   optim_param_value                   \n",
       "XGBoosting (RR)              booster            gblinear                30.069229   \n",
       "                                                gbtree                4130.807396   \n",
       "                                                dart                          NaN   \n",
       "Random Forest Regressor (RR) bootstrap          True                   686.905354   \n",
       "                                                False                 1135.938024   \n",
       "LSTM-AD                      window_size        2.0                   7212.296172   \n",
       "                                                1.5                   7187.589350   \n",
       "                                                True                  7100.157630   \n",
       "                                                0.5                   6811.646270   \n",
       "                             lstm_layers        True                  6886.480288   \n",
       "                                                3                     7212.047548   \n",
       "                                                10.0                  7211.625977   \n",
       "ImageEmbeddingCAE            latent_size        70                     759.405784   \n",
       "                                                130                    720.849037   \n",
       "                                                100                    606.300005   \n",
       "GrammarViz                   paa_transform_size 5                             NaN   \n",
       "                                                3                             NaN   \n",
       "                                                4                             NaN   \n",
       "                             alphabet_size      6                             NaN   \n",
       "                                                4                             NaN   \n",
       "                                                5                             NaN   \n",
       "                                                3                             NaN   \n",
       "\n",
       "                                                                  execute_main_time  \\\n",
       "                                                                               mean   \n",
       "algorithm                    optim_param_name   optim_param_value                     \n",
       "XGBoosting (RR)              booster            gblinear                   6.179861   \n",
       "                                                gbtree                     7.550251   \n",
       "                                                dart                            NaN   \n",
       "Random Forest Regressor (RR) bootstrap          True                       7.337085   \n",
       "                                                False                      6.875242   \n",
       "LSTM-AD                      window_size        2.0                      517.001906   \n",
       "                                                1.5                      987.811766   \n",
       "                                                True                     894.732521   \n",
       "                                                0.5                      487.481813   \n",
       "                             lstm_layers        True                     231.708138   \n",
       "                                                3                        657.695031   \n",
       "                                                10.0                    1827.328234   \n",
       "ImageEmbeddingCAE            latent_size        70                        25.712897   \n",
       "                                                130                       25.040023   \n",
       "                                                100                       25.297295   \n",
       "GrammarViz                   paa_transform_size 5                         23.461379   \n",
       "                                                3                         19.362655   \n",
       "                                                4                         20.302092   \n",
       "                             alphabet_size      6                         21.203208   \n",
       "                                                4                         19.905535   \n",
       "                                                5                         14.728401   \n",
       "                                                3                          9.743828   \n",
       "\n",
       "                                                                  repetition  \n",
       "                                                                       count  \n",
       "algorithm                    optim_param_name   optim_param_value             \n",
       "XGBoosting (RR)              booster            gblinear                 168  \n",
       "                                                gbtree                   168  \n",
       "                                                dart                     168  \n",
       "Random Forest Regressor (RR) bootstrap          True                     168  \n",
       "                                                False                    168  \n",
       "LSTM-AD                      window_size        2.0                      193  \n",
       "                                                1.5                      193  \n",
       "                                                True                     193  \n",
       "                                                0.5                      193  \n",
       "                             lstm_layers        True                     193  \n",
       "                                                3                        193  \n",
       "                                                10.0                     193  \n",
       "ImageEmbeddingCAE            latent_size        70                       168  \n",
       "                                                130                      168  \n",
       "                                                100                      168  \n",
       "GrammarViz                   paa_transform_size 5                        168  \n",
       "                                                3                        168  \n",
       "                                                4                        168  \n",
       "                             alphabet_size      6                        168  \n",
       "                                                4                        168  \n",
       "                                                5                        168  \n",
       "                                                3                        168  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_by = (\"ROC_AUC\", \"mean\")\n",
    "metric_agg_type = [\"min\", \"mean\", \"median\"]\n",
    "time_agg_type = \"mean\"\n",
    "aggs = {\n",
    "    \"PR_AUC\": metric_agg_type,\n",
    "    \"ROC_AUC\": metric_agg_type,\n",
    "    \"train_main_time\": time_agg_type,\n",
    "    \"execute_main_time\": time_agg_type,\n",
    "    \"repetition\": \"count\"\n",
    "}\n",
    "\n",
    "df_tmp = df.reset_index()\n",
    "df_tmp = df_tmp.groupby(by=[\"algorithm\", \"optim_param_name\", \"optim_param_value\"]).agg(aggs)\n",
    "df_tmp = df_tmp.reset_index()\n",
    "df_tmp = df_tmp.sort_values(by=[\"algorithm\", \"optim_param_name\", sort_by], ascending=False)\n",
    "df_tmp = df_tmp.set_index([\"algorithm\", \"optim_param_name\", \"optim_param_value\"])\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selected parameters\n",
    "\n",
    "- XGBoosting (RR): `booster=gblinear`\n",
    "- Random Forest Regressor (RR): `bootstrap=True`\n",
    "- LSTM-AD: `window_size=\"2.0 * dataset period size\",lstm_layers=1` (window_size: higher ist better, lstm_layers: lower is better)\n",
    "- ImageEmbeddingCAE: `latent_size=70`\n",
    "- GrammarViz: `paa_transform_size=5,alphabet_size=6`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"XGBoosting (RR)\", \"booster\", \"gblinear\"),\n",
    "    (\"XGBoosting (RR)\", \"booster\", \"gbtree\")\n",
    "], \"sinus-combined-diff-2\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"GrammarViz\", \"paa_transform_size\", 5),\n",
    "    (\"GrammarViz\", \"paa_transform_size\", 3)\n",
    "], \"poly-diff-count-5\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval38",
   "language": "python",
   "name": "timeeval38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
